Advanced Diagnosis of Polycystic Ovarian Syndrome using Machine Learning and Multimodal Data Integration

PCOS is a common endocrine disorder that impacts women in their reproductive years characterized by irregular menstrual cycles, hyperandrogenism, and polycystic ovaries. Polycystic Ovary Syndrome (PCOS) presents significant challenges in diagnosis due to its heterogeneous nature and varied clinical manifestations. This project aimed to develop a comprehensive system for PCOS detection, integrating ultrasound images and clinical data through advanced machine learning techniques, using Rotterdam criteria for diagnostic decisions. Feature extraction from ultrasound images was conducted using the ResNet-50 deep learning model, while clinical data underwent correlation-based feature selection. Three classification algorithms - Support Vector Machine (SVM), Random Forest and Logistic Regression - were used to categorize the extracted features from ultrasound images. The integration of image-based and clinical-based features was explored and evaluated to have better accuracy revealing the potential for enhancing PCOS diagnosis accuracy. The developed system holds promise for assisting doctors in PCOS diagnosis, offering a holistic approach that leverages both imaging and clinical information.

@article{M2024,
  title     = {Advanced Diagnosis of Polycystic Ovarian Syndrome using Machine Learning and Multimodal Data Integration},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Nethra Sai M and Sakthivel V and Prakash P and Vishnukumar K and Dugki Min},
  doi       = {10.14569/IJACSA.2024.01506122},
  url       = {https://doi.org/10.14569/IJACSA.2024.01506122}
}